Solving the $H_{0}$ tension in $f(T)$ Gravity through Bayesian Machine Learning

Understand

Bayesian Machine Learning~(BML) and strong lensing time delay~(SLTD) techniques are used in order to tackle the $H_{0}$ tension in $f(T)$ gravity.

• The power of BML relies on employing a model-based generative process which already plays an important role in different domains of cosmology and astrophysics, being the present work a further proof of this.
• Three viable $f(T)$ models are considered: a power law, an exponential, and a squared exponential model.
• The learned constraints and respective results indicate that the exponential model, $f(T)=\alpha T_{0}\left(1-e^{-p T / T_{0}}\right)$, has the capability to solve the $H_{0}$ tension quite efficiently.